system

Abstract

The system according to this embodiment aims to efficiently generate relevant content based on specified themes and keywords. [Solution] The system according to the embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit receives input of a theme or keyword. The generation unit generates relevant content based on the information received by the reception unit. The provision unit provides the content generated by the generation unit.

Description

Technical Field

[0001] The technology of the present disclosure relates to a system.

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance as a response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to efficiently generate related content based on a specified theme or keyword.

[0005] The system according to the embodiment aims to efficiently generate related content based on a specified theme or keyword.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, a generation unit, and a provision unit. The reception unit receives an input of a theme or keyword. The generation unit generates related content based on the information received by the reception unit. The provision unit provides the content generated by the generation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently generate relevant content based on specified themes and keywords. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

[0011] In the following embodiments, the signed processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Furthermore, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include CPU (Central Processing Unit), GPU (Graphics Processing Unit), GPGPU (General-Purpose computing on Graphics Processing Units), APU (Accelerated Processing Unit), or TPU (Tensor Processing Unit).

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

[0013] In the following embodiments, the signed storage is one or more non-volatile storage devices that store various programs and various parameters. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes.

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more事项 connected by "and / or", the same concept as "A and / or B" is applied.

[0016] [First Embodiment] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0017] As shown in FIG. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0018] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0023] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0024] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device (e.g., a generation server) may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​assistant for automatic content generation according to an embodiment of the present invention is a system that generates relevant content based on specified themes and keywords, learns the user's style and preferences, and adapts to individual needs. The AI ​​assistant for automatic content generation allows the user to input a theme or keyword, and the AI ​​generates relevant content based on that input and provides the generated content to the user. This system can learn the user's style and preferences and adapt to individual needs. For example, the user can input themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." This information is input to the AI. Next, the AI ​​analyzes the input information and generates relevant content. For example, if the theme "travel" is input, the AI ​​can generate articles, photos, videos, etc., related to travel. Also, if the keyword "Paris" is input, the AI ​​can generate tourist information about Paris, restaurant recommendations, etc. The generated content is provided to the user. The user can view the generated content and use it according to their interests. For example, they can read articles about travel or refer to tourist information about Paris. Furthermore, this system can learn the user's style and preferences and adapt to individual needs. The AI ​​analyzes the user's past input and browsing history to learn the user's style and preferences. This allows it to prioritize the generation of content that the user will like. For example, if a user has viewed a lot of content related to "cooking" in the past, the AI ​​can prioritize the generation of content related to "cooking". The AI ​​can also estimate the user's emotions and adjust the way the content is presented based on those emotions. For example, if a user wants to relax, the AI ​​can generate relaxing content. Conversely, if a user is seeking stimulation, the AI ​​can generate stimulating content. In this way, the AI ​​assistant for automatic content generation of the present invention can generate relevant content based on specified themes and keywords, learn the user's style and preferences, and adapt to individual needs.This allows users to easily obtain content tailored to their interests and preferences. It also enables AI assistants that support automated content generation to provide content that meets user needs.

[0029] The AI ​​assistant for automatic content generation according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit accepts the input of a theme or keyword. For example, a user can input a theme such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception unit transmits the input theme or keyword to the AI. The generation unit uses the AI ​​to generate relevant content based on the information received by the reception unit. For example, if the theme "travel" is input, the generation unit can generate articles, photos, videos, etc., related to travel. Also, if the keyword "Paris" is input, the generation unit can generate tourist information, restaurant introductions, etc., related to Paris. The generation unit uses a generation AI to collect relevant information based on the input theme or keyword and generate content. For example, the generation AI can collect information from the internet and generate relevant articles, images, and videos. The provision unit provides the content generated by the generation unit to the user. For example, the provision unit displays the generated articles, images, and videos on the user's device. The provision unit makes the generated content viewable by the user. As a result, the AI ​​assistant for automatic content generation according to the embodiment can generate and provide relevant content based on themes and keywords. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate content using a generation AI model that takes themes and keywords as input and outputs relevant content. Some or all of the above-described processes in the provision unit may be performed using, for example, an AI, or without an AI. For example, the provision unit can provide content using an AI model that displays the generated content on the user's device.

[0030] The reception desk accepts themes or keywords as input. For example, users can enter themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception desk then sends the entered themes and keywords to the AI. Specifically, the reception desk accepts input through a user interface. The user interface can take the form of a web browser or mobile application and is designed to be intuitive for users to operate. For example, if a user enters the theme "travel," the reception desk receives the theme in text format and sends it to the internal data processing system. The reception desk appropriately analyzes the entered themes and keywords and performs necessary preprocessing before sending them to the AI. For example, if the entered keywords are ambiguous, the reception desk can ask the user for additional information to complete them. Also, if there are multiple themes or keywords entered, it appropriately classifies them and converts them into a data structure for sending to the AI. Furthermore, the reception desk also has a function to automatically suggest relevant themes and keywords based on the user's input history and past search patterns. This allows users to generate the desired content more efficiently. The reception desk can process user input in real time and quickly send it to the AI, thereby improving the overall response speed of the system.

[0031] The generation unit uses AI to generate relevant content based on information received by the reception unit. For example, if the theme "travel" is entered, the generation unit can generate articles, photos, and videos related to travel. Similarly, if the keyword "Paris" is entered, the generation unit can generate tourist information and restaurant recommendations related to Paris. The generation unit uses generation AI to collect relevant information based on the entered theme and keywords and generate content. Specifically, the generation AI uses natural language processing (NLP) technology to analyze information on the internet and generate relevant articles, images, and videos. For example, the generation AI analyzes the text of a webpage, extracts important information, and generates an article. It can also use image recognition technology to select relevant images and add appropriate captions. Furthermore, the generation AI can use video generation technology to edit relevant videos and provide them to the user. The generation unit evaluates the quality of the generated content and makes corrections and improvements as needed. For example, it checks the grammar and expression of generated articles and corrects any errors. It also evaluates the quality of generated images and videos to ensure they do not contain inappropriate content. This allows the generation unit to provide users with high-quality content. Furthermore, the generation unit continuously improves the AI's algorithm based on user feedback, enabling the creation of more accurate content.

[0032] The Provider unit provides users with content generated by the Generator unit. For example, the Provider unit displays generated articles, images, and videos on the user's device. The Provider unit makes the generated content viewable. Specifically, the Provider unit displays the generated content to the user through a web browser or mobile application. For example, generated articles are displayed as web pages, and users can scroll to view the content. Images and videos are displayed in an interactive gallery format, and users can click or tap to view details. The Provider unit employs responsive design to provide a display format optimized for the user's device. This allows for comfortable content viewing on various devices such as desktops, tablets, and smartphones. Furthermore, the Provider unit also has a function to automatically recommend relevant content based on the user's browsing history and preferences. For example, if a user views an article about "travel," the Provider unit will recommend related articles on "tourist destinations" and "travel guides." This allows users to efficiently find content that interests them. The Provider unit also has a function to notify users in real time of updates to the generated content. For example, when a new article is generated, users can be notified via push notification or email. This ensures that users always receive the latest information.

[0033] The generation unit can generate relevant content based on learned data that reflects the user's style or preferences. For example, the generation unit can analyze the user's past browsing and input history to learn their style and preferences. For instance, if the user has viewed a lot of content related to "cooking" in the past, the generation unit can prioritize generating content related to "cooking." The generation unit can also generate content based on styles and genres that the user has previously enjoyed viewing. For example, the generation unit can generate content based on formats (articles, videos, etc.) that the user has previously enjoyed viewing. This allows for content tailored to individual needs by generating content based on the user's style and preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate content using a generative AI model that takes the user's style and preferences as input and outputs relevant content.

[0034] The reception desk can analyze the user's past input history of themes or keywords and select an appropriate input method. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. It can also set text input as the default if the user has preferred it in the past. Furthermore, if the reception desk has previously entered information during a specific time period, it can send a notification prompting the user to enter information during that time period. This allows the reception desk to provide the user with the most suitable input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select an input method using an AI model that takes past input history as input and outputs an appropriate input method.

[0035] The reception desk can filter the input of themes or keywords based on the user's current areas of interest. For example, if the user is currently interested in "travel," the reception desk will prioritize displaying travel-related themes and keywords. Similarly, if the user is interested in "cooking," the reception desk can prioritize displaying cooking-related themes and keywords. Furthermore, if the user is interested in "technology," the reception desk can prioritize displaying technology-related themes and keywords. This allows the system to provide more relevant themes and keywords by filtering based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform filtering using an AI model that takes the user's current areas of interest as input and outputs filtered themes and keywords.

[0036] The reception desk can prioritize inputting themes or keywords that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific city, the reception desk will prioritize displaying themes and keywords related to that city. Similarly, if the user is in a specific country, the reception desk can prioritize displaying themes and keywords related to that country. Furthermore, if the user is in a specific region, the reception desk can prioritize displaying themes and keywords related to that region. This allows for the provision of more relevant themes and keywords by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can prioritize inputting themes and keywords using an AI model that takes geographical location information as input and outputs highly relevant themes and keywords.

[0037] The reception desk can analyze the user's social media activity and input relevant themes or keywords. For example, if the user frequently posts about "travel" on social media, the reception desk will prioritize displaying travel-related themes and keywords. Similarly, if the user frequently posts about "cooking," the reception desk can prioritize displaying cooking-related themes and keywords. Furthermore, if the user frequently posts about "technology," the reception desk can prioritize displaying technology-related themes and keywords. This allows the system to provide themes and keywords relevant to the user by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input themes and keywords using an AI model that takes social media activity as input and outputs relevant themes and keywords.

[0038] The generation unit can adjust the level of detail of the generated content based on the importance of the theme or keyword. For example, if the theme or keyword is important, the generation unit will generate content containing detailed information. Alternatively, if the theme or keyword is general, the generation unit can generate content containing concise information. Furthermore, if the theme or keyword is specific and niche, the generation unit can generate content containing specialized information. This allows for the provision of more appropriate content by adjusting the level of detail based on the importance of the theme or keyword. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes the importance of the theme or keyword as input and outputs the level of detail of the generated content.

[0039] The generation unit can generate relevant content based on learned data that reflects the user's style or preferences. For example, the generation unit can generate content based on the style the user has previously enjoyed viewing. It can also generate content based on the genres the user has previously enjoyed viewing. Furthermore, the generation unit can generate content based on the format (articles, videos, etc.) the user has previously enjoyed viewing. This allows for content to be adapted to individual needs by generating content based on the user's style and preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can generate content using a generative AI model that takes the user's style and preferences as input and outputs relevant content.

[0040] The generation unit can determine the generation priority based on the submission timing of themes or keywords when generating content. For example, the generation unit will prioritize generating content for the most recent themes or keywords. It can also postpone generating content for older themes or keywords. Furthermore, if themes or keywords are related to a specific event, the generation unit can generate content to coincide with that event. This allows for the provision of more appropriate content by determining the generation priority based on the submission timing of themes and keywords. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can determine the priority using a generation AI model that takes the submission timing of themes and keywords as input and outputs the generation priority.

[0041] The generation unit can adjust the generation order based on the relevance of themes or keywords during content generation. For example, the generation unit can prioritize generating content for themes or keywords with high relevance. It can also prioritize generating content for themes or keywords with moderate relevance. Furthermore, it can generate content last for themes or keywords with low relevance. This allows for the provision of more appropriate content by adjusting the generation order based on the relevance of themes and keywords. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the order using a generation AI model that takes the relevance of themes and keywords as input and outputs the generation order.

[0042] The service provider can select the optimal display method by referring to the user's past browsing history at the time of delivery. For example, the service provider can display content based on the style the user has previously preferred to view. It can also display content based on the genre the user has previously preferred to view. Furthermore, the service provider can display content based on the format (articles, videos, etc.) the user has previously preferred to view. In this way, by referring to past browsing history, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past browsing history as input and outputs the optimal display method.

[0043] The service provider can customize the content it delivers based on the user's current areas of interest. For example, if the user is currently interested in "travel," the service provider will prioritize displaying travel-related content. Similarly, if the user is interested in "cooking," the service provider can prioritize displaying cooking-related content. Furthermore, if the user is interested in "technology," the service provider can prioritize displaying technology-related content. This allows for the delivery of more relevant content by customizing it based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For instance, the service provider can customize content using an AI model that takes the user's current areas of interest as input and outputs customized content.

[0044] The content provider can provide optimal content by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific city, the provider can prioritize displaying content related to that city. Furthermore, if the user is in a specific country, the provider can prioritize displaying content related to that country. In addition, if the user is in a specific region, the provider can prioritize displaying content related to that region. This allows for the provision of more relevant content by considering geographical location information. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the content provider can provide content using an AI model that takes geographical location information as input and outputs optimal content.

[0045] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0046] The content generation unit can analyze a user's past purchase history and generate relevant content. For example, if a user has purchased many travel-related products in the past, the generation unit will prioritize generating travel-related content. Similarly, if a user has purchased many cooking-related products, the generation unit can generate cooking-related content. Furthermore, if a user has purchased many products from a specific brand, it can generate content related to that brand. This allows content tailored to the user's interests and preferences by leveraging their purchase history.

[0047] The generation unit can analyze a user's social media activity and generate relevant content. For example, if a user frequently posts about "travel" on social media, the generation unit will prioritize generating travel-related content. Similarly, if a user frequently posts about "cooking," it can generate cooking-related content. Furthermore, if a user frequently posts about "technology," it can generate technology-related content. This allows the system to leverage social media activity to provide content tailored to the user's interests and concerns.

[0048] The reception system can prioritize the input of highly relevant themes and keywords by considering the user's geographical location. For example, if a user is in a specific city, the system will prioritize displaying themes and keywords related to that city. Similarly, if a user is in a specific country, the system can prioritize displaying themes and keywords related to that country. Furthermore, if a user is in a specific region, the system can prioritize displaying themes and keywords related to that region. This allows the system to provide more relevant themes and keywords by considering the user's geographical location.

[0049] The service provider can select the optimal display method by referring to the user's past browsing history. For example, it can display content based on the style the user has previously preferred to view. It can also display content based on the genre the user has previously preferred to view. Furthermore, it can display content based on the format (articles, videos, etc.) the user has previously preferred to view. In this way, by referring to past browsing history, the service provider can provide the user with the most suitable display method.

[0050] The content delivery system can provide optimal content by considering the user's geographical location. For example, if a user is in a specific city, content related to that city can be displayed preferentially. Similarly, if a user is in a specific country, content related to that country can be displayed preferentially. Furthermore, if a user is in a specific region, content related to that region can be displayed preferentially. This allows for the provision of more relevant content by considering geographical location.

[0051] The following briefly describes the processing flow for example form 1.

[0052] Step 1: The reception desk accepts themes or keywords. For example, users can enter themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception desk then sends the entered themes or keywords to the AI. Step 2: The generation unit uses AI to generate relevant content based on the information received by the reception unit. For example, if the theme "travel" is entered, the generation unit can generate articles, photos, and videos related to travel. Also, if the keyword "Paris" is entered, the generation unit can generate tourist information and restaurant introductions related to Paris. The generation unit uses generation AI to collect relevant information based on the entered theme and keywords and generate content. For example, the generation AI can collect information from the internet and generate relevant articles, images, and videos. Step 3: The provider unit provides the user with the content generated by the generator unit. For example, the provider unit displays the generated articles, images, and videos on the user's device. The provider unit makes the user able to view the generated content.

[0053] (Example of form 2) The AI ​​assistant for automatic content generation according to an embodiment of the present invention is a system that generates relevant content based on specified themes and keywords, learns the user's style and preferences, and adapts to individual needs. The AI ​​assistant for automatic content generation allows the user to input a theme or keyword, and the AI ​​generates relevant content based on that input and provides the generated content to the user. This system can learn the user's style and preferences and adapt to individual needs. For example, the user can input themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." This information is input to the AI. Next, the AI ​​analyzes the input information and generates relevant content. For example, if the theme "travel" is input, the AI ​​can generate articles, photos, videos, etc., related to travel. Also, if the keyword "Paris" is input, the AI ​​can generate tourist information about Paris, restaurant recommendations, etc. The generated content is provided to the user. The user can view the generated content and use it according to their interests. For example, they can read articles about travel or refer to tourist information about Paris. Furthermore, this system can learn the user's style and preferences and adapt to individual needs. The AI ​​analyzes the user's past input and browsing history to learn the user's style and preferences. This allows it to prioritize the generation of content that the user will like. For example, if a user has viewed a lot of content related to "cooking" in the past, the AI ​​can prioritize the generation of content related to "cooking". The AI ​​can also estimate the user's emotions and adjust the way the content is presented based on those emotions. For example, if a user wants to relax, the AI ​​can generate relaxing content. Conversely, if a user is seeking stimulation, the AI ​​can generate stimulating content. In this way, the AI ​​assistant for automatic content generation of the present invention can generate relevant content based on specified themes and keywords, learn the user's style and preferences, and adapt to individual needs.This allows users to easily obtain content tailored to their interests and preferences. It also enables AI assistants that support automated content generation to provide content that meets user needs.

[0054] The AI ​​assistant for automatic content generation according to this embodiment comprises a reception unit, a generation unit, and a provision unit. The reception unit accepts the input of a theme or keyword. For example, a user can input a theme such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception unit transmits the input theme or keyword to the AI. The generation unit uses the AI ​​to generate relevant content based on the information received by the reception unit. For example, if the theme "travel" is input, the generation unit can generate articles, photos, videos, etc., related to travel. Also, if the keyword "Paris" is input, the generation unit can generate tourist information, restaurant introductions, etc., related to Paris. The generation unit uses a generation AI to collect relevant information based on the input theme or keyword and generate content. For example, the generation AI can collect information from the internet and generate relevant articles, images, and videos. The provision unit provides the content generated by the generation unit to the user. For example, the provision unit displays the generated articles, images, and videos on the user's device. The provision unit makes the generated content viewable by the user. As a result, the AI ​​assistant for automatic content generation according to the embodiment can generate and provide relevant content based on themes and keywords. Some or all of the above-described processes in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can generate content using a generation AI model that takes themes and keywords as input and outputs relevant content. Some or all of the above-described processes in the provision unit may be performed using, for example, an AI, or without an AI. For example, the provision unit can provide content using an AI model that displays the generated content on the user's device.

[0055] The reception desk accepts themes or keywords as input. For example, users can enter themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception desk then sends the entered themes and keywords to the AI. Specifically, the reception desk accepts input through a user interface. The user interface can take the form of a web browser or mobile application and is designed to be intuitive for users to operate. For example, if a user enters the theme "travel," the reception desk receives the theme in text format and sends it to the internal data processing system. The reception desk appropriately analyzes the entered themes and keywords and performs necessary preprocessing before sending them to the AI. For example, if the entered keywords are ambiguous, the reception desk can ask the user for additional information to complete them. Also, if there are multiple themes or keywords entered, it appropriately classifies them and converts them into a data structure for sending to the AI. Furthermore, the reception desk also has a function to automatically suggest relevant themes and keywords based on the user's input history and past search patterns. This allows users to generate the desired content more efficiently. The reception desk can process user input in real time and quickly send it to the AI, thereby improving the overall response speed of the system.

[0056] The generation unit uses AI to generate relevant content based on information received by the reception unit. For example, if the theme "travel" is entered, the generation unit can generate articles, photos, and videos related to travel. Similarly, if the keyword "Paris" is entered, the generation unit can generate tourist information and restaurant recommendations related to Paris. The generation unit uses generation AI to collect relevant information based on the entered theme and keywords and generate content. Specifically, the generation AI uses natural language processing (NLP) technology to analyze information on the internet and generate relevant articles, images, and videos. For example, the generation AI analyzes the text of a webpage, extracts important information, and generates an article. It can also use image recognition technology to select relevant images and add appropriate captions. Furthermore, the generation AI can use video generation technology to edit relevant videos and provide them to the user. The generation unit evaluates the quality of the generated content and makes corrections and improvements as needed. For example, it checks the grammar and expression of generated articles and corrects any errors. It also evaluates the quality of generated images and videos to ensure they do not contain inappropriate content. This allows the generation unit to provide users with high-quality content. Furthermore, the generation unit continuously improves the AI's algorithm based on user feedback, enabling the creation of more accurate content.

[0057] The Provider unit provides users with content generated by the Generator unit. For example, the Provider unit displays generated articles, images, and videos on the user's device. The Provider unit makes the generated content viewable. Specifically, the Provider unit displays the generated content to the user through a web browser or mobile application. For example, generated articles are displayed as web pages, and users can scroll to view the content. Images and videos are displayed in an interactive gallery format, and users can click or tap to view details. The Provider unit employs responsive design to provide a display format optimized for the user's device. This allows for comfortable content viewing on various devices such as desktops, tablets, and smartphones. Furthermore, the Provider unit also has a function to automatically recommend relevant content based on the user's browsing history and preferences. For example, if a user views an article about "travel," the Provider unit will recommend related articles on "tourist destinations" and "travel guides." This allows users to efficiently find content that interests them. The Provider unit also has a function to notify users in real time of updates to the generated content. For example, when a new article is generated, users can be notified via push notification or email. This ensures that users always receive the latest information.

[0058] The generation unit can generate relevant content based on learned data that reflects the user's style or preferences. For example, the generation unit can analyze the user's past browsing and input history to learn their style and preferences. For instance, if the user has viewed a lot of content related to "cooking" in the past, the generation unit can prioritize generating content related to "cooking." The generation unit can also generate content based on styles and genres that the user has previously enjoyed viewing. For example, the generation unit can generate content based on formats (articles, videos, etc.) that the user has previously enjoyed viewing. This allows for content tailored to individual needs by generating content based on the user's style and preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the generation unit can generate content using a generative AI model that takes the user's style and preferences as input and outputs relevant content.

[0059] The reception unit can estimate the user's emotions and adjust the timing of theme or keyword input based on the estimated emotions. For example, if the user is feeling stressed, the reception unit can delay the input timing to allow them to relax. The reception unit can also send an immediate notification prompting input if the user is concentrating. Furthermore, if the user is tired, the reception unit can extend the input timing to encourage a break. By adjusting the input timing based on the user's emotions, the user can input themes and keywords at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not using AI. For example, the reception unit can adjust the input timing using an AI model that takes the user's emotion data as input and outputs the input timing.

[0060] The reception desk can analyze the user's past input history of themes or keywords and select an appropriate input method. For example, if the reception desk has frequently used voice input in the past, it will prioritize suggesting voice input. It can also set text input as the default if the user has preferred it in the past. Furthermore, if the reception desk has previously entered information during a specific time period, it can send a notification prompting the user to enter information during that time period. This allows the reception desk to provide the user with the most suitable input method by analyzing past input history. Some or all of the above processing in the reception desk may be performed using AI, or not. For example, the reception desk can select an input method using an AI model that takes past input history as input and outputs an appropriate input method.

[0061] The reception desk can filter the input of themes or keywords based on the user's current areas of interest. For example, if the user is currently interested in "travel," the reception desk will prioritize displaying travel-related themes and keywords. Similarly, if the user is interested in "cooking," the reception desk can prioritize displaying cooking-related themes and keywords. Furthermore, if the user is interested in "technology," the reception desk can prioritize displaying technology-related themes and keywords. This allows the system to provide more relevant themes and keywords by filtering based on the user's current areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can perform filtering using an AI model that takes the user's current areas of interest as input and outputs filtered themes and keywords.

[0062] The reception unit can estimate the user's emotions and determine the priority of input themes or keywords based on the estimated emotions. For example, if the user is relaxed, the reception unit will prioritize displaying relaxing themes and keywords. It can also prioritize stimulating themes and keywords if the user is excited. Furthermore, if the user is tired, it can prioritize simple and low-burden themes and keywords. This allows for the provision of more appropriate content by prioritizing themes and keywords based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, or not. For example, the reception unit can determine priorities using an AI model that takes user emotion data as input and outputs themes and keywords in order of priority.

[0063] The reception desk can prioritize inputting themes or keywords that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific city, the reception desk will prioritize displaying themes and keywords related to that city. Similarly, if the user is in a specific country, the reception desk can prioritize displaying themes and keywords related to that country. Furthermore, if the user is in a specific region, the reception desk can prioritize displaying themes and keywords related to that region. This allows for the provision of more relevant themes and keywords by considering geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For instance, the reception desk can prioritize inputting themes and keywords using an AI model that takes geographical location information as input and outputs highly relevant themes and keywords.

[0064] The reception desk can analyze the user's social media activity and input relevant themes or keywords. For example, if the user frequently posts about "travel" on social media, the reception desk will prioritize displaying travel-related themes and keywords. Similarly, if the user frequently posts about "cooking," the reception desk can prioritize displaying cooking-related themes and keywords. Furthermore, if the user frequently posts about "technology," the reception desk can prioritize displaying technology-related themes and keywords. This allows the system to provide themes and keywords relevant to the user by analyzing their social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or without AI. For example, the reception desk can input themes and keywords using an AI model that takes social media activity as input and outputs relevant themes and keywords.

[0065] The generation unit can estimate the user's emotions and adjust the way the generated content is expressed based on the estimated emotions. For example, if the user is relaxed, the generation unit will generate content in a calm tone. If the user is excited, the generation unit can also generate content in an energetic tone. Furthermore, if the user is sad, the generation unit can generate content in a comforting tone. This allows for the provision of more appropriate content by adjusting the way the content is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using a generative AI, or not. For example, the generation unit can adjust the expression using a generative AI model that takes user emotion data as input and outputs a way of expressing the content.

[0066] The generation unit can adjust the level of detail of the generated content based on the importance of the theme or keyword. For example, if the theme or keyword is important, the generation unit will generate content containing detailed information. Alternatively, if the theme or keyword is general, the generation unit can generate content containing concise information. Furthermore, if the theme or keyword is specific and niche, the generation unit can generate content containing specialized information. This allows for the provision of more appropriate content by adjusting the level of detail based on the importance of the theme or keyword. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can adjust the level of detail using a generation AI model that takes the importance of the theme or keyword as input and outputs the level of detail of the generated content.

[0067] The generation unit can generate relevant content based on learned data that reflects the user's style or preferences. For example, the generation unit can generate content based on the style the user has previously enjoyed viewing. It can also generate content based on the genres the user has previously enjoyed viewing. Furthermore, the generation unit can generate content based on the format (articles, videos, etc.) the user has previously enjoyed viewing. This allows for content to be adapted to individual needs by generating content based on the user's style and preferences. Some or all of the above-described processes in the generation unit may be performed using, for example, a generative AI, or not. For example, the generation unit can generate content using a generative AI model that takes the user's style and preferences as input and outputs relevant content.

[0068] The generation unit can estimate the user's emotions and adjust the length of the generated content based on the estimated emotions. For example, if the user is relaxed, the generation unit can generate longer content. It can also generate shorter content if the user is in a hurry. Furthermore, if the user is focused, the generation unit can generate content of an appropriate length. This allows for the provision of more appropriate content by adjusting the length based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using a generative AI, or not. For example, the generation unit can adjust the length using a generative AI model that takes user emotion data as input and outputs the length of the content.

[0069] The generation unit can determine the generation priority based on the submission timing of themes or keywords when generating content. For example, the generation unit will prioritize generating content for the most recent themes or keywords. It can also postpone generating content for older themes or keywords. Furthermore, if themes or keywords are related to a specific event, the generation unit can generate content to coincide with that event. This allows for the provision of more appropriate content by determining the generation priority based on the submission timing of themes and keywords. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the generation unit can determine the priority using a generation AI model that takes the submission timing of themes and keywords as input and outputs the generation priority.

[0070] The generation unit can adjust the generation order based on the relevance of themes or keywords during content generation. For example, the generation unit can prioritize generating content for themes or keywords with high relevance. It can also prioritize generating content for themes or keywords with moderate relevance. Furthermore, it can generate content last for themes or keywords with low relevance. This allows for the provision of more appropriate content by adjusting the generation order based on the relevance of themes and keywords. Some or all of the above processing in the generation unit may be performed using, for example, a generation AI, or not. For example, the generation unit can adjust the order using a generation AI model that takes the relevance of themes and keywords as input and outputs the generation order.

[0071] The service provider can estimate the user's emotions and adjust the display method of the content based on the estimated emotions. For example, if the user is relaxed, the service provider can display the content in calm colors. If the user is excited, the service provider can also display the content in vivid colors. Furthermore, if the user is tired, the service provider can display the content in highly visible colors. This allows for the provision of more appropriate content by adjusting the display method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can adjust the display method using an AI model that takes user emotion data as input and outputs a display method.

[0072] The service provider can select the optimal display method by referring to the user's past browsing history at the time of delivery. For example, the service provider can display content based on the style the user has previously preferred to view. It can also display content based on the genre the user has previously preferred to view. Furthermore, the service provider can display content based on the format (articles, videos, etc.) the user has previously preferred to view. In this way, by referring to past browsing history, the service provider can provide the user with the most suitable display method. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can select a display method using an AI model that takes past browsing history as input and outputs the optimal display method.

[0073] The service provider can customize the content it delivers based on the user's current areas of interest. For example, if the user is currently interested in "travel," the service provider will prioritize displaying travel-related content. Similarly, if the user is interested in "cooking," the service provider can prioritize displaying cooking-related content. Furthermore, if the user is interested in "technology," the service provider can prioritize displaying technology-related content. This allows for the delivery of more relevant content by customizing it based on the user's current areas of interest. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For instance, the service provider can customize content using an AI model that takes the user's current areas of interest as input and outputs customized content.

[0074] The content provider can estimate the user's emotions and determine the priority of the content to be provided based on the estimated emotions. For example, if the user is relaxed, the provider will prioritize displaying relaxing content. If the user is excited, the provider can also prioritize displaying stimulating content. Furthermore, if the user is tired, the provider can prioritize displaying simple and low-stress content. This allows for the provision of more appropriate content by prioritizing content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the content provider may be performed using AI, or not. For example, the content provider can determine priorities using an AI model that takes user emotion data as input and outputs content priorities.

[0075] The content provider can provide optimal content by considering the user's geographical location information at the time of delivery. For example, if the user is in a specific city, the provider can prioritize displaying content related to that city. Furthermore, if the user is in a specific country, the provider can prioritize displaying content related to that country. In addition, if the user is in a specific region, the provider can prioritize displaying content related to that region. This allows for the provision of more relevant content by considering geographical location information. Some or all of the above processing in the content provider may be performed using AI, for example, or without AI. For example, the content provider can provide content using an AI model that takes geographical location information as input and outputs optimal content.

[0076] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0077] The reception unit can analyze the user's voice input and automatically extract themes and keywords using speech recognition technology. For example, if a user says, "I'm looking for my next travel destination," the reception unit will extract the theme "travel" and send it to the generation unit. Similarly, if a user says, "I want to learn a new recipe," the reception unit can extract the keyword "recipe." Furthermore, the reception unit can estimate the user's emotions from their voice tone and speaking style and adjust the input content based on the estimated emotions. For example, if a user is speaking excitedly, the reception unit can prioritize extracting more stimulating themes and keywords. This allows for the efficient extraction of themes and keywords that meet the user's needs by utilizing voice input.

[0078] The content generation unit can analyze a user's past purchase history and generate relevant content. For example, if a user has purchased many travel-related products in the past, the generation unit will prioritize generating travel-related content. Similarly, if a user has purchased many cooking-related products, the generation unit can generate cooking-related content. Furthermore, if a user has purchased many products from a specific brand, it can generate content related to that brand. This allows content tailored to the user's interests and preferences by leveraging their purchase history.

[0079] The reception desk can acquire the user's biometric information and adjust themes and keywords based on their health status. For example, if a user's heart rate or blood pressure is high, it can prioritize displaying relaxing themes and keywords. It can also suggest relaxing content if the user's sleep quality is poor. Furthermore, if the user's activity level is low, it can prioritize displaying health-related themes and keywords. This allows the system to utilize biometric information to provide themes and keywords tailored to the user's health condition.

[0080] The generation unit can analyze a user's social media activity and generate relevant content. For example, if a user frequently posts about "travel" on social media, the generation unit will prioritize generating travel-related content. Similarly, if a user frequently posts about "cooking," it can generate cooking-related content. Furthermore, if a user frequently posts about "technology," it can generate technology-related content. This allows the system to leverage social media activity to provide content tailored to the user's interests and concerns.

[0081] The content delivery system can estimate the user's emotions and adjust how content is displayed based on those estimates. For example, if the user is relaxed, the content can be displayed in calming colors. If the user is excited, the content can be displayed in vibrant colors. Furthermore, if the user is tired, the content can be displayed in highly visible colors. By adjusting how content is displayed based on the user's emotions, more appropriate content can be provided.

[0082] The reception system can prioritize the input of highly relevant themes and keywords by considering the user's geographical location. For example, if a user is in a specific city, the system will prioritize displaying themes and keywords related to that city. Similarly, if a user is in a specific country, the system can prioritize displaying themes and keywords related to that country. Furthermore, if a user is in a specific region, the system can prioritize displaying themes and keywords related to that region. This allows the system to provide more relevant themes and keywords by considering the user's geographical location.

[0083] The generation unit can estimate the user's emotions and adjust the length of the generated content based on those emotions. For example, if the user is relaxed, it can generate longer content. If the user is in a hurry, it can generate shorter content. Furthermore, if the user is focused, it can generate content of an appropriate length. By adjusting the length of content based on the user's emotions, it can provide more appropriate content.

[0084] The service provider can select the optimal display method by referring to the user's past browsing history. For example, it can display content based on the style the user has previously preferred to view. It can also display content based on the genre the user has previously preferred to view. Furthermore, it can display content based on the format (articles, videos, etc.) the user has previously preferred to view. In this way, by referring to past browsing history, the service provider can provide the user with the most suitable display method.

[0085] The content delivery system can estimate the user's emotions and prioritize the content delivered based on those emotions. For example, if the user is relaxed, it can prioritize displaying relaxing content. If the user is excited, it can prioritize displaying stimulating content. Furthermore, if the user is tired, it can prioritize displaying simple and low-stress content. By prioritizing content based on the user's emotions, it is possible to deliver more appropriate content.

[0086] The content delivery system can provide optimal content by considering the user's geographical location. For example, if a user is in a specific city, content related to that city can be displayed preferentially. Similarly, if a user is in a specific country, content related to that country can be displayed preferentially. Furthermore, if a user is in a specific region, content related to that region can be displayed preferentially. This allows for the provision of more relevant content by considering geographical location.

[0087] The following briefly describes the processing flow for example form 2.

[0088] Step 1: The reception desk accepts themes or keywords. For example, users can enter themes such as "travel" or "cooking," or keywords such as "Paris" or "recipes." The reception desk then sends the entered themes or keywords to the AI. Step 2: The generation unit uses AI to generate relevant content based on the information received by the reception unit. For example, if the theme "travel" is entered, the generation unit can generate articles, photos, and videos related to travel. Also, if the keyword "Paris" is entered, the generation unit can generate tourist information and restaurant introductions related to Paris. The generation unit uses generation AI to collect relevant information based on the entered theme and keywords and generate content. For example, the generation AI can collect information from the internet and generate relevant articles, images, and videos. Step 3: The provider unit provides the user with the content generated by the generator unit. For example, the provider unit displays the generated articles, images, and videos on the user's device. The provider unit makes the user able to view the generated content.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0091] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] For example, the reception unit is implemented by the reception device 38 of the smart device 14, allowing the user to input themes and keywords. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, generating relevant content based on the input themes and keywords. The provision unit is implemented by the output device 40 of the smart device 14, providing the generated content to the user. For example, the generation unit may be implemented by the specific processing unit 290 of the data processing device 12, and the provision unit may be implemented by the control unit 46A of the smart device 14. The correspondence between each unit and the devices and control units is not limited to the examples described above, and various modifications are possible.

[0093] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0094] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0095] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0096] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0097] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0098] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0099] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0100] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0101] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0102] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0103] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0104] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0107] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] For example, the reception unit is implemented by the microphone 238 of the smart glasses 214, allowing the user to input themes and keywords by voice. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, generating relevant content based on the input themes and keywords. The provision unit is implemented by the speaker 240 of the smart glasses 214, providing the generated content to the user by voice. For example, the generation unit may be implemented by the specific processing unit 290 of the data processing device 12, and the provision unit may be implemented by the control unit 46A of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0109] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0110] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0111] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0112] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0113] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0115] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0116] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0117] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0118] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0119] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0120] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0121] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0122] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0123] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0124] For example, the reception unit is implemented by the microphone 238 of the headset terminal 314, allowing the user to input themes and keywords by voice. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, generating relevant content based on the input themes and keywords. The provision unit is implemented by the display 343 of the headset terminal 314, displaying the generated content to the user. For example, the generation unit may be implemented by the specific processing unit 290 of the data processing device 12, and the provision unit may be implemented by the control unit 46A of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0125] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0126] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0127] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0128] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0129] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0130] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0131] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0132] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0133] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the reception unit is implemented by the microphone 238 of the robot 414, allowing the user to input themes and keywords by voice. The generation unit is implemented by the specific processing unit 290 of the data processing device 12, generating relevant content based on the input themes and keywords. The delivery unit is implemented by the speaker 240 of the robot 414, providing the generated content to the user by voice. For example, the generation unit may be implemented by the specific processing unit 290 of the data processing device 12, and the delivery unit may be implemented by the control unit 46A of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0143] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0144] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0145] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0146] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0147] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0148] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0149] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0150] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0151] Alternatively, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 in response to a request from the data processing device 12.

[0152] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0153] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0154] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0155] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0156] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0157] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0158] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0159] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0160] (Note 1) A system comprising: a reception unit that accepts themes or keywords as input; a generation unit that generates related content based on the information received by the reception unit; and a provision unit that provides the content generated by the generation unit. (Note 2) The generation unit is a system that generates relevant content based on learning results that have been acquired regarding the user's style or preferences. (Note 3) The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme or keyword input based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned reception unit is a system that analyzes the user's past theme or keyword input history and selects the appropriate input method. (Note 5) The aforementioned reception unit is When entering a theme or keyword, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is It estimates the user's emotions and determines the priority of themes or keywords to be entered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception system prioritizes input of themes or keywords that are highly relevant to the user, taking into account the user's geographical location information. (Note 8) The aforementioned reception unit is a system that analyzes the user's social media activity and inputs related themes or keywords. (Note 9) The generating unit is It estimates the user's emotions and adjusts the way content is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned generation unit is a system that adjusts the level of detail of content generation based on the importance of themes or keywords. (Note 11) The generating unit is The system generates relevant content based on the user's style or preferences, using learned learning results. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is It estimates the user's emotions and adjusts the length of the generated content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned generation unit is a system that determines the generation priority based on the timing of theme or keyword submission when generating content. (Note 14) The generation unit is a system that adjusts the generation order based on the relevance of themes or keywords when generating content. (Note 15) The aforementioned supply unit is, It estimates the user's emotions and adjusts how content is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, When providing content, the system selects the optimal display method by referring to the user's past browsing history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned provisioning unit is a system that customizes the content provided at the time of provision based on the user's current areas of interest. (Note 18) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of the content to be delivered based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned provisioning unit is a system that provides optimal content by taking into account the user's geographical location information at the time of provision. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception desk that accepts themes or keywords, A generation unit that generates related content based on the information received by the reception unit, The system comprises a providing unit that provides the content generated by the generation unit. system.

2. The generation unit generates relevant content based on the learning results, which are learned from the user's style or preferences. The system according to feature 1.

3. The aforementioned reception unit is It estimates the user's emotions and adjusts the timing of theme or keyword input based on the estimated emotions. The system according to feature 1.

4. The reception unit analyzes the user's past theme or keyword input history and selects an appropriate input method. The system according to feature 1.

5. The aforementioned reception unit is When entering a theme or keyword, filtering is performed based on the user's current areas of interest. The system according to feature 1.

6. The aforementioned reception unit is It estimates the user's emotions and determines the priority of themes or keywords to be entered based on those estimated emotions. The system according to feature 1.

7. The reception unit prioritizes inputting themes or keywords that are highly relevant to the user, taking into account the user's geographical location information. The system according to feature 1.

8. The reception unit analyzes the user's social media activity and inputs relevant themes or keywords. The system according to feature 1.

Images